最近在看GCN相关的论文，按照谱方法、非谱方法和几个框架的结构总结了一下。推荐给大家，有时间的话可以读一下。
综述类
Deep Learning on Graphs: A Survey
Ziwei Zhang, Peng Cui, Wenwu Zhu
Graph Neural Networks: A Review of Methods and Application
Jie Zhou, Ganqu Cui, Zhengyan Zhang, Cheng Yang, Zhiyuan Liu, Maosong Sun
谱方法
SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
ThomasN.Kipf，Max Welling
Convolutional Neural Networkson Graphs with Fast Localized Spectral Filtering
Mich aël Defferrard, Xavier Bresson, Pierre Vandergheynst
非谱方法
**PATCHY-SAN **
Learning Convolutional Neural Networks for Graphs
Mathias Niepert， Mohamed Ahmed ，Konstantin Kutzko ** Neural FPs **
Convolutional networks on graphs for learning molecular fingerprints
David Duvenaud, Dougal Maclaurin, Jorge Aguilera-Iparraguirre, Rafael Gómez-Bombarelli, Timothy Hirzel, Alán Aspuru-Guzik
**DCNN **
Diffusion-ConvolutionalNeuralNetworks
James Atwood, Don Towsley.
**DGCN **
Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification
ChenyiZhuang,QiangMa
框架
**MONET **
Geometric deep learning on graphs and manifolds using mixture model CNNs
Federico Monti, Davide Boscaini, Jonathan Masci, Emanuele Rodolà, Jan Svoboda, Michael M. Bronstein
GNs
Relational inductive biases, deep learning, and graph networks
Battaglia, Peter W and Hamrick, Jessica B and Bapst, Victor and Sanchez-Gonzalez, Alvaro and Zambaldi, Vinicius and Malinowski, Mateusz and Tacchetti, Andrea and Raposo, David and Santoro, Adam and Faulkner, Ryan and others.
MPNNS
Neural Message Passing for Quantum Chemistry
Gilmer, Justin and Schoenholz, Samuel S and Riley, Patrick F and Vinyals, Oriol and Dahl, George E
**graphsage **
Inductive Representation Learning on Large Graphs
William L. Hamilton, Rex Ying, Jure Leskovec
上面只是总结了我看的相关的论文，GCN还有很多模型待看，但是整体的思路可以分成上面谱方法和非谱方法的分类来看。
